import re

import numpy as np
import torch


class BaseRecLabelDecode(object):
    """Convert between text-label and text-index."""

    def __init__(self, character_dict_path=None, use_space_char=False):
        self.beg_str = 'sos'
        self.end_str = 'eos'
        self.reverse = False
        self.character_str = []

        if character_dict_path is None:
            self.character_str = '0123456789abcdefghijklmnopqrstuvwxyz'
            dict_character = list(self.character_str)
        else:
            with open(character_dict_path, 'rb') as fin:
                lines = fin.readlines()
                for line in lines:
                    line = line.decode('utf-8').strip('\n').strip('\r\n')
                    self.character_str.append(line)
            if use_space_char:
                self.character_str.append(' ')
            dict_character = list(self.character_str)
            if 'arabic' in character_dict_path:
                self.reverse = True

        dict_character = self.add_special_char(dict_character)
        self.dict = {}
        for i, char in enumerate(dict_character):
            self.dict[char] = i
        self.character = dict_character

    def pred_reverse(self, pred):
        pred_re = []
        c_current = ''
        for c in pred:
            if not bool(re.search('[a-zA-Z0-9 :*./%+-]', c)):
                if c_current != '':
                    pred_re.append(c_current)
                pred_re.append(c)
                c_current = ''
            else:
                c_current += c
        if c_current != '':
            pred_re.append(c_current)

        return ''.join(pred_re[::-1])

    def add_special_char(self, dict_character):
        return dict_character

    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
        """convert text-index into text-label."""
        result_list = []
        ignored_tokens = self.get_ignored_tokens()
        batch_size = len(text_index)
        for batch_idx in range(batch_size):
            selection = np.ones(len(text_index[batch_idx]), dtype=bool)
            if is_remove_duplicate:
                selection[1:] = text_index[batch_idx][1:] != text_index[
                    batch_idx][:-1]
            for ignored_token in ignored_tokens:
                selection &= text_index[batch_idx] != ignored_token

            char_list = [
                self.character[text_id]
                for text_id in text_index[batch_idx][selection]
            ]
            if text_prob is not None:
                conf_list = text_prob[batch_idx][selection]
            else:
                conf_list = [1] * len(selection)
            if len(conf_list) == 0:
                conf_list = [0]

            text = ''.join(char_list)

            if self.reverse:  # for arabic rec
                text = self.pred_reverse(text)

            result_list.append((text, np.mean(conf_list).tolist()))
        return result_list

    def get_ignored_tokens(self):
        return [0]  # for ctc blank

    def get_character_num(self):
        return len(self.character)


class CTCLabelDecode(BaseRecLabelDecode):
    """Convert between text-label and text-index."""

    def __init__(self,
                 character_dict_path=None,
                 use_space_char=False,
                 **kwargs):
        super(CTCLabelDecode, self).__init__(character_dict_path,
                                             use_space_char)

    def __call__(self, preds, batch=None, *args, **kwargs):
        # preds = preds['res']
        if isinstance(preds, torch.Tensor):
            preds = preds.detach().cpu().numpy()
        preds_idx = preds.argmax(axis=2)
        preds_prob = preds.max(axis=2)
        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
        if batch is None:
            return text
        label = self.decode(batch[1].cpu().numpy())
        return text, label

    def add_special_char(self, dict_character):
        dict_character = ['blank'] + dict_character
        return dict_character
